From 4a62bd65688c7ba09e02355beb33a0c7ebe843e9 Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Thu, 20 May 2021 13:06:27 -0400 Subject: [PATCH] quiz links, quizzes 7 and 8, Regression Lesson 1 --- Introduction/3-ethics/README.md | 4 +- Regression/1-Tools/README.md | 4 +- Regression/2-Data/README.md | 4 +- Regression/3-Linear/README.md | 4 +- Regression/4-Logistic/README.md | 4 +- quiz-app/src/assets/translations/en.json | 326 +++++++++++++++++++++++ 6 files changed, 336 insertions(+), 10 deletions(-) diff --git a/Introduction/3-ethics/README.md b/Introduction/3-ethics/README.md index b80cd1a5..e2cfec22 100644 --- a/Introduction/3-ethics/README.md +++ b/Introduction/3-ethics/README.md @@ -7,7 +7,7 @@ Add a sketchnote if possible/appropriate [![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI") > Video: Microsoft's Approach to Responsible AI -## [Pre-lecture quiz](link-to-quiz-app) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/) Describe what we will learn @@ -54,7 +54,7 @@ Add a challenge for students to work on collaboratively in class to enhance the Optional: add a screenshot of the completed lesson's UI if appropriate -## [Post-lecture quiz](link-to-quiz-app) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/) ## Review & Self Study diff --git a/Regression/1-Tools/README.md b/Regression/1-Tools/README.md index 7add459d..6ae40387 100644 --- a/Regression/1-Tools/README.md +++ b/Regression/1-Tools/README.md @@ -3,7 +3,7 @@ ![Logistic vs. Linear Regression Infographic](./images/logistic-linear.png) > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/) ## Introduction The lessons in this section cover types of Regression in the context of machine learning. Regression models can help determine the relationship between variables. This type of model can predict values such as length, temperature, or age, thus uncovering relationships between variables as it analyzes datapoints. @@ -177,7 +177,7 @@ Congratulations, you just built your first Linear Regression model, created a pr ## 🚀Challenge Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/) ## Review & Self Study diff --git a/Regression/2-Data/README.md b/Regression/2-Data/README.md index 1fa203b8..357d8c20 100644 --- a/Regression/2-Data/README.md +++ b/Regression/2-Data/README.md @@ -3,7 +3,7 @@ > ![Data Vizualization Infographic](./images/data-visualization.png) > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/) ### Introduction @@ -143,7 +143,7 @@ This is a more useful data visualization! It seems to indicate that the highest ## 🚀Challenge Explore the different types of visualization that matplotlib offers. Which types are most appropriate for regression problems? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/) ## Review & Self Study diff --git a/Regression/3-Linear/README.md b/Regression/3-Linear/README.md index 64d4d8cd..95326e82 100644 --- a/Regression/3-Linear/README.md +++ b/Regression/3-Linear/README.md @@ -2,7 +2,7 @@ ![Linear vs Polynomial Regression Infographic](./images/linear-polynomial.png) > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/) ### Introduction So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this unit. You have also visualized it using Matplotlib. Now you are ready to dive deeper into regression for ML. In this lesson, you will learn more about two types of regression: basic linear regression and polynomial regression, along with some of the math underlying these techniques. @@ -249,7 +249,7 @@ It does make sense! And, if this is a better model than the previous one, lookin Test several different variables in this notebook to see how correlation corresponds to model accuracy. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/) ## Review & Self Study diff --git a/Regression/4-Logistic/README.md b/Regression/4-Logistic/README.md index 13550353..020f3fba 100644 --- a/Regression/4-Logistic/README.md +++ b/Regression/4-Logistic/README.md @@ -1,5 +1,5 @@ # Logistic Regression to Predict Categories -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/) ### Introduction @@ -218,7 +218,7 @@ In future lessons on classifications, you will learn how to iterate to improve y ## 🚀Challenge There's a lot more to unpack regarding Logistic Regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://kaggle.com) for interesting datasets. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/) ## Review & Self Study diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json index 52520aad..ac43dd23 100644 --- a/quiz-app/src/assets/translations/en.json +++ b/quiz-app/src/assets/translations/en.json @@ -109,6 +109,332 @@ ] } ] + }, + { + "id": 3, + "title": "Lesson 1 - Intro to ML: Post-Lecture Quiz", + "quiz": [ + { + "questionText": "q1", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q2", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "true" + }, + { + "answerText": "b", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q3", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 4, + "title": "Lesson 1 - Intro to ML: Post-Lecture Quiz", + "quiz": [ + { + "questionText": "q1", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q2", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "true" + }, + { + "answerText": "b", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q3", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 5, + "title": "Lesson 1 - Intro to ML: Post-Lecture Quiz", + "quiz": [ + { + "questionText": "q1", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q2", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "true" + }, + { + "answerText": "b", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q3", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 6, + "title": "Lesson 1 - Intro to ML: Post-Lecture Quiz", + "quiz": [ + { + "questionText": "q1", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q2", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "true" + }, + { + "answerText": "b", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q3", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 7, + "title": "Lesson 7 - Get started with Python and Scikit-Learn for Regression models: Pre-Lecture Quiz", + "quiz": [ + { + "questionText": "Which of these variables is a numeric variable?", + "answerOptions": [ + { + "answerText": "Height", + "isCorrect": "true" + }, + { + "answerText": "Gender", + "isCorrect": "false" + }, + { + "answerText": "Hair Color", + "isCorrect": "false" + } + ] + }, + { + "questionText": "Which of these variables is a categorical variable?", + "answerOptions": [ + { + "answerText": "Heart Rate", + "isCorrect": "false" + }, + { + "answerText": "Blood Type", + "isCorrect": "true" + }, + { + "answerText": "Weight", + "isCorrect": "false" + } + ] + }, + { + "questionText": "Which of these problems is a Regression analysis-based problem?", + "answerOptions": [ + { + "answerText": "Predicting the final exam marks of a student", + "isCorrect": "true" + }, + { + "answerText": "Predicting the blood type of a person", + "isCorrect": "false" + }, + { + "answerText": "Predicting whether an email is spam or not", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 8, + "title": "Lesson 8 - Get started with Python and Scikit-Learn for Regression models: Post-Lecture Quiz", + "quiz": [ + { + "questionText": "If your Machine Learning model's training accuracy is 95 % and the testing accuracy is 30 %, then what type of condition it is called?", + "answerOptions": [ + { + "answerText": "Overfitting", + "isCorrect": "true" + }, + { + "answerText": "Underfitting", + "isCorrect": "false" + }, + { + "answerText": "Double Fitting", + "isCorrect": "false" + } + ] + }, + { + "questionText": "The process of identifying significant features from a set of features is called:", + "answerOptions": [ + { + "answerText": "Feature Extraction", + "isCorrect": "false" + }, + { + "answerText": "Feature Dimensionality Reduction", + "isCorrect": "false" + }, + { + "answerText": "Feature Selection", + "isCorrect": "true" + } + ] + }, + { + "questionText": "The process of splitting a dataset into a certain ratio of training and testing dataset using Scikit Learn's 'train_test_split()' method/function is called:", + "answerOptions": [ + { + "answerText": "Cross-Validation", + "isCorrect": "false" + }, + { + "answerText": "Hold-Out Validation", + "isCorrect": "true" + }, + { + "answerText": "Leave one out Validation", + "isCorrect": "false" + } + ] + } + ] } ] }